AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces
By Mahmoud Afifi, Abdelrahman Abdelhamed. 2017
110 citations recorded by the source corpus.
Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition. Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Shiguang Shan
- Xilin Chen
- Morgan Klaus Scheuerman
- Kandrea Wade
- Yong Li
- Caitlin Lustig
- Jiabei Zeng
- Jed R. Brubaker
In Signal Processing
- Morgan Klaus Scheuerman
- Shiguang Shan
- Kandrea Wade
- Yong Li
- Caitlin Lustig
- Jiabei Zeng
- Jed R. Brubaker
- Xilin Chen
Citations in the corpus
- How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “For example, although the original LFW database did not have explicit gender annotations, Affi et al. have subsequently annotated this database with gender annotations [7] on their official website.”
- Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism (2019, methodology): “It is worth mentioning that previous work on person reidentification ( [22]) gender classfication ( [23]) and FER ([24]) has also adopted methods of unifying global and local features.”